Globalization, Inequality, And the Rich Countries of the G20 - Evidence From the Luxembourg Income Study

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ISSN: 1525-3066

Center for Policy ResearchWorking Paper No. 48

GLOBALIZATION,INEQUALITY, AND THE RICH COUNTRIES OF THE G-20:EVIDENCE FROM THE LUXEMBOURG INCOME STUDY (LIS)

Timothy M. Smeeding

Center for Policy ResearchMaxwell School o f Citizenship and Public Af fairsSyracuse University

426 Eggers HallSyracuse, New York 13244-1020

(315) 443-3114 | Fax (315) 443-1081e-mail: [email protected]

November 2002

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Up-to-date information about CPRs research projects and other activities is availablefrom our World Wide Web site at www-cpr.maxwell.syr.edu. All recent working

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CENTER FOR POLICY RESEARCH Spring 2003

Timothy Smeeding, DirectorProfessor of Economics & Public Admin istration

__________

Associate Directors

Margaret Austin Douglas Holtz-EakinAssociate Director, Professor of Economics

Budget and Administration Associate Director, Center for Policy Research

Douglas Wolf J ohn YingerProfessor of Public Administration Professor of Economics and Public Administration

Associate Director, Aging Studies Program Associate Director, Metropolitan Studies Program

SENIOR RESEARCH ASSOCIATES

Scott Allard ............................. Public AdministrationDan Black ............................................... EconomicsArthur Brooks ........................ Public AdministrationStacy Dickert-Conlin ............................... EconomicsWilliam Duncombe ................. Public AdministrationGary Engelhardt ................... ................. Economics

Deborah Freund ................... . Public AdministrationVernon Greene .................... . Public AdministrationMadonna Harrington Meyer ..................... SociologyChristine Himes ........................................ SociologyWilliam C. Horrace ................................. EconomicsBernard Jump ........................ Public AdministrationDuke Kao ............................................... EconomicsEric Kingson ......................... ............... Social Work

Thomas Kniesner ................. ................. EconomicsJ eff Kubik ............................................... EconomicsAndrew London ....................................... Sociology

J erry Miner ............................................. EconomicsJ ohn Moran .......................... ................. EconomicsJ an Ondrich ........................................... Economics

J ohn Palmer ........................... Public AdministrationLori P loutz-Snyder .. Health and Physical Education

J eff Racine ............................................ EconomicsGrant Reeher ................................. Political ScienceStuart Rosenthal ................... ................. EconomicsMichael Wasylenko................................ Economics

J anet Wilmoth .......................................... Sociology

GRADUATE ASSOCIATES

Anna Amirkhanyan..... ............ Public AdministrationBeth Ashby ............................................. EconomicsEldar Beiseitov ...................................... EconomicsCaroline Bourdeaux ............... Public AdministrationChristine Caffrey ...................................... SociologyGabby Chapman .................................... Economics

Yong Chen ............................................. EconomicsSeng Eun Choi ...................................... EconomicsCarrie Cochran ....................... Public AdministrationChristopher Cunningham ....................... EconomicsSarah Douglas ....................... Public Administration

Tae Ho Eom ........................... Public AdministrationYing Fang ................................................ SociologyAmy Fedigan .......................... Public Administration

J ose Galdo ............................................. EconomicsAndrzej Grodner ..................................... EconomicsGlenda Gross ........................................... Sociology

J erry Kalarickal ..................................... EconomicsAnil Kumar ............................................. EconomicsKristina Lambright ................. Public AdministrationXiaoli Liang ............................................ EconomicsLiqun Liu ............................................... EconomicsAlison Louie ........................... Public Administration

J oseph Marchand .................................. EconomicsCristian Meghea ................................... EconomicsEmily Pas ............................................. EconomicsAdriana Sandu ....................... Public Administration

J on Schwabish ..................................... EconomicsClaudia Smith ........................................ EconomicsSara Smits ..............................................SociologyLora Walters ......................... Public Administration

Wen Wang ............................. Public AdministrationJ ames Williamson.................................. EconomicsBo Zhao .................................................Economics

STAFF

Kelly Bogart ....................... Administrative SecretaryMartha Bonney ....... Publications/Events CoordinatorKaren Cimilluca ............. Librarian/Office CoordinatorKim Desmond ................... Administrative SecretaryKati Foley ..................... Administrative Assistant, LISEmily NaPier .............. Senior Secretary/Receptionist

Kitty Nasto .......................... Administrative Secretary

Candi Patterson ....................... Computer ConsultantDenise Paul .......................... Editorial Assistant, NTJMary Santy ......................... Administrative SecretaryMindy Tanner ..................... Administrative Secretary


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Abstract

The purpose of this study is to summarize and comment upon what we know about the

determinants of both the level and trend in economic inequality over the past two decades, and torelate these findings to the progress of globalization in these nations. While the fruits of

economic progress in rich nations have not been equally spread, we argue that most citizens in

rich Organization for Economic Cooperation and Development (OECD) nations have benefitedfrom the trend toward global economic progress. We begin with a summary of the differences in

overall economic inequality within the G-20 nations based on LIS (Luxembourg Income Study)

data and recent work by others. Here we find that social policies, wage distributions, time

worked, social and labor market institutions and demographic differences all have someinfluence on why there are large differences in inequality among rich nations at any point in

time. In contrast, trade policy has not been shown to have any major impact on economic

inequality.

Next, we turn to trends in inequality. We find modest and sometimes dissimilar changesin the distribution of income have taken place within most advanced nations, with most finding a

higher level of inequality in the mid-to-late 1990s than in the 1980s. Inequality, however, has notrisen markedly in some nations (e.g., Denmark, Germany, France, and Canada) over this period,

while its rise has slowed in several other nations during the late 1990s. The explanations for

rising inequality in rich countries are many, and no one single set of explanations is ultimatelyconvincing. In particular, there is no evidence that we know of that trade and globalization is bad

for rich countries.

This suggests that rising economic inequality is not inevitable, or that it necessarily hurts

low skill-low income families. Rather it suggests that globalization does not force any singleoutcome on any country. Domestic policies and institutions still have large effects on the level

and trend of inequality within rich and middle-income nations, even in a globalizing worldeconomy.


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I. Introduction: Cross-National Studies of Income Distr ibution

Increasingly, the rich and poor nations of the world face a common set of social and

economic trends and policy issues: the cost of population aging, changing family structures

(including a growing number of single parent families in many nations), the growing majority of

two-earner families, increasing numbers of immigrants from poorer nations. In particular most

rich and middle-income nations are experiencing rising economic inequality generated by skill-

biased technological change (marked by rising returns to higher labor market skills),

international trade and other factors related to the globalization, of the world economy. While

increasing economic inequality is not inevitable, and while public policy and labor market

institutions can help prevent many of the downside effects of these trends, the facts of the matter

are that income inequality has continued to increase in the large majority of the worlds rich

nations, over the past decade (Atkinson 2000; Friedman 2000; Gottschalk, Gustafsson, and

Palmer 1997; Smeeding and Grodner 2000). All of these rich nations have also designed systems

of social protection to shield their citizens against the risk of a fall in economic status due to

unemployment, divorce, disability, retirement, and death of a spouse. The interaction of

economic and demographic forces and social programs generates the distribution of net

disposable income in each of these nations.

The recent evidence on the level and trend in economic and social inequality in rich and

middle-income nations is the major topic of this brief paper. The emergence and availability of

cross-nationally comparable databases has put us in a position to directly compare the

experiences of rich nations in coping with the growth of market income inequality, and to begin

to add middle-income nations as well. Additional comparable data of the type called for by the


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Canberra Report (Canberra Group 2001) will also allow better studies of this same type in

coming years for a wider still range of countries.

The Luxembourg Income Study (LIS) project has pioneered the availability of online data

that allows researchers to use microdata to measure inequality and to test their ideas and

hypotheses about the sources and causes of that inequality using modern methods. One of the

major purposes of this paper is to update the facts and figures in these reports by presenting

evidence on the level and trend in income inequality as portrayed by the LIS data, and from other

sources. We begin with a brief review of methodology. Then we turn briefly to the results for

level of inequality. Trends in inequality come next and they are often more difficult to precisely

assess than are levels, whether using LIS or other sources. We also include a brief discussion of

recent research on the determinants of these levels and trends.

Comparisons of these experiences may help us to understand how one nation is similar to

and different from other nations. It may also help us trace these differences to their economic

demographic, and policy-related sources. The institutions, which emerge in nations to help

mitigate the forces of market-driven economic inequality, are also of interest. Global trade will

benefit some groups and hurt (at least temporarily) some others, even when the overall benefits

exceed the costs for any nation as a whole (Friedman 2000). Too often we forget that greater

trade brings with it wider choices, better products, and better prices which benefit all citizens,

regardless of their personal changes in earnings or incomes.

Cross-national research has also taught us that every nation must design its own set of social

and economic policies tempered by its institutions, values, culture, and politics. And the

conclusions of this paper are that these national policies continue to matter greatly.


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II. Measuring Economic Inequality: The Basics

Here we briefly review the sources of our evidence and their strengths and weaknesses.

There is currently a set of international standards for income distribution that parallel the

international standards used for systems of national income accounts, that have been pioneered

by the Canberra Group (2001).1

The Luxembourg Income Study (LIS), which underlies much of

this paper and the initial findings of the Canberra group, offers a place to start with these

analyses. In fact the LIS definition of annual disposable income is the starting point from which

this paper begins. LIS offers the reader many choices of perspective in terms of country, income

measure, accounting unit, and time frame. But its relatively short time frame (1979-1997 for

most nations, but 1968-1997 for five countries), and limited number of observation periods per

country (three to five periods per country at present), currently limits its usefulness for studying

longer-term trends in income distribution. The purpose of this section of the paper is to explain

the choices we have made in our use of LIS. The choices we, and others, have made to study

longer-term trends in income distribution are more fully discussed in Gottschalk and Smeeding

(1997, 2000) and Atkinson, Rainwater, and Smeeding (1995). It is important to note also that

these income definitions are also the ones that have been initially used by the Inter-American

Development Bank (IDB) in their work on this topic (Szeleky and Hilgert 1999a, 1999b) and are

the starting point for the Canberra Group (2001) work on cross nationally comparable income

data.

Our attention is focused here on the distribution of disposable money income that is cash

and near-cash money income, including earnings of all household members, after direct taxes

and including transfer payments. Several points should be noted about this choice:

income rather than consumption is taken as the indicator of economic well-being. Wealth

is ignored except to the extent that it is represented by cash interest, rent, and dividends.


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While for developing countries, consumption is liable to be a better definition and also

very close to disposable income, we use income here;

the LIS definition of income falls considerably short of a comprehensive definition,

typically excluding much of capital gains, imputed rents, and most income in-kind (with

the exception of near-cash benefits and the measurement of home production in Mexico

and Russian LIS surveys; Canberra Report 2001, chapter 8). But it is also much wider

than the distribution of wages or earnings per worker used in much of the globalization

literature;

no account is taken of indirect taxes or of the benefits from public spending (other than

cash and near-cash transfers) such as those from health care, education, or most housing

subsidies;

the period of income measurement is in general the calendar year with income measured

on an annual basis.2

Thus, variables measured may be less than ideal and results may not be fully comparable

across countries. For example, it might be that one country may help low-income families

through money benefits (included in cash income), whereas another provides subsidized housing,

childcare, or education (which is not taken into account). And some types of benefits, e.g.,

education, may have quite different effects on longer-term national well-being. While one study

(Smeeding, et al. 1993) finds that the distribution of housing, education, and health care benefits

reinforces the general differences in income distribution for a subset of the western nations

examined there, there is no guarantee that these relationships hold for alternative countries or

methods of accounting (Gardiner, et al. 1995), nor that they are stable over a longer time frame.

In fact, most studies show that countries, which spend more for cash benefits, tend to also spend

more for noncash benefits. Because noncash benefits are more equally distributed than are cash


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benefits, levels of inequality within high noncash spending countries are lessened, but the same

rank ordering of these countries, with respect to inequality levels that are found here using cash

alone, persists when noncash benefits are added in. And while we use income, not consumption,

as the basis for our comparisons, due to the relative ease of measurement and comparability of

the former, there is evidence that consumption inequalities are similar to income inequalities in

major European nations and in the United States (Hagenaars, deVos, and Zaidi 1998; Johnson

and Smeeding 1997).

The distribution of disposable income requires answers to both the what and the among

whom questions. Regarding the former, earned income from wages, salaries, self-employment,

cash property income (but not capital gains or losses), and other private cash income transfers

(occupational pensions, alimony, and child support) or market income, is the primary source of

disposable income for most families. To reach the disposable income concept used in this paper,

we add public transfer payments (social retirement, family allowances, unemployment

compensation, income support benefits) and deduct personal income tax and social security

contributions from market income. Near-cash benefitsthose that are virtually equivalent to

cash (food stamps in the United States and housing allowances in the United Kingdom and

Sweden)are also included in the disposable income measure used here.

The question of distribution among whom is answered among individuals. When

assessing disposable income inequality, however, the unit of aggregation is the household: the

incomes of all household members are aggregated and then divided by an equivalence scale to

arrive at individual equivalent income. The equivalence scale used in the square root of

household size and all LIS-based income measures in this paper use this equivalence scale and

the adjusted disposable income concept which is produced by dividing (unadjusted) disposable

income by family size raised to the power .5 (square root of family size). This is the same scale


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used in Atkinson, Rainwater, and Smeeding (1995) (see also, Buhmann, Rainwater, Schmaus,

and Smeeding 1988).

For the most part, the householdall persons sharing the same housing unit regardless of

familial relationshipis the common unit of analysis.3

Complete intra-household income sharing

is assumed, despite the fact that members of the same household probably do not equally share in

all household resources. To assume that unrelated individuals living with others do not at all

share in common household incomes or household public goods (heat, durables, etc.) is a

worse assumption in our judgment. Thus, our unit of account is the household.

The approach adopted here, based in large part on data from the Luxembourg Income Study

(LIS), overcomes some, but not all, of the problems of making comparisons across countries and

across time that plagued earlier studies. Some problems, for example, the use of data from

different types of sources, still remain. But all of the data used in the analysis of levels of

inequality are drawn from household income surveys, or their equivalent, and in no case is

synthetic data used. One major advantage of LIS is the availability of micro-data. The aim of the

LIS project has been to assemble a single database containing survey data from many countries

that is as consistent as possible. Access to the micro-data means that it is possible to produce

results on the same basis, starting from individual household records, and to test their sensitivity

to alternative choices of units, definition, and other concepts. It is therefore possible to make any

desired adjustment for household size. Aggregate adjustments, such as that from pre-tax (market

income) to post-tax (disposable) income are not necessary, although in some cases imputations

are necessary at the household level. The data all cover, at least in principle, the whole non-

institutionalized population though the treatment of immigrants may differ across nations. These

data are supplemented here by data provided by one major nation not yet a member of LIS

(Japan) where a national expert calculated income inequality measures with the consultation of


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the LIS staff (Ishikawa 1996), and by a recent LIS paper which adds Latin America estimates of

similarly defined disposable income (Szekely and Hilgert 1999a; 1999b). The rest of the

calculations were made by the author and the LIS project team. Many of the results cited here are

directly available from the LIS home pages key figures section

(http://www.lisproject.org/keyfigures/ineqtable.htm).

While the aim of the LIS project is to increase the degree of cross-national comparability,

complete cross-national comparability is not possible, even if we were to administer our own

surveys in each nation. Comparability is a matter of degree, and all that one can hope for is to

reach an acceptably high level. In economic and statistical terms, the data is noisy, but the ratio

of signal to noise is reduced by LIS. Ultimately, the reader must decide the acceptability of the

evidence before them. To skeptics, we can offer that most of the cross-national results provided

here have been reviewed by a team of national expertsstatisticians, social scientists, and policy

analystsprior to their publication by the United Nations, Organization for Economic

Cooperation and Development (OECD) and in other forums, and they have appeared in refereed

journals. And, because the LIS data is ultimately available to the research community at zero

economic cost, researchers are free to repeat these calculations themselves. Moreover, recent

attempts to mimic the LIS definitions by the IDB are used to demonstrate the value of these

techniques for a wider range of nations, such as the G-20.

III. Comparing Levels of Inequality at a Point in Time

The LIS data sets are used here to compare the distribution of disposable income in 26 or

more nations during the 1990s. We focus here on relative (Figure 1) income differences, not

absolute income differences.4

The relative inequality patterns found here correspond roughly to

the results found in Atkinson, Rainwater, and Smeeding (1995), which use earlier years LIS


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data in most cases. Our choices of inequality measures are four: the ratio of the income of the

person at the bottom and top 10th

percentiles to the median, P10 and P90, respectively; the ratio of

the income of the person at the 90th

percentile to the person at the 10th

percentilethe decile

ratio(a measure of social distance); and the gini coefficient.

Relative Differences in Inequality Across Nations

We begin with a chart containing all four measures of inequality with the LIS nations

ordered by the decile ratio from lowest to highest. At the bottom of Figure 1, we find Mexico

with a low-income person at the 10th

percentile in 1998 (P10) having an income that is 28 percent

of the median, followed by Russia at 30 and the United States at 38. A high-income person at the

90th percentile (P90), in contrast, has 328 percent of the median in Mexico, 282 percent in Russia

and 214 percent in the United States. The Mexican, Russian, and United States decile ratios are

11.55, 9.39 and 5.57, respectively, meaning the income of the typical high income person is

more than 11.5, 9.3 or 5.5 times the income of the typical low-income person, even after we have

adjusted for taxes, transfers, and family size. In contrast, the average low-income person has 49

percent of the income of the middle person in the average country; the average rich person has

195 percent as much, and the decile ratio shows an average economic distance between rich

and poor of 4.2 times P10.

At the other end of the chart, a Swedish citizen at P10 has 60 percent of the median, the P90

is 156 and the decile ratio is 2.61, less than one-half as large as the United States value, and one-

quarter or less of the Russian or Mexican values. This evidence suggests that the range of

inequality and of social distance between rich and poor in the rich and medium-income nations

of the world is rather large in the mid-1990s. It also begs for comparable information for

additional middle-income and developing nations of the world.


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Countries in Figure 1 fall into clusters, with inequality the least in Scandinavia (Finland,

Sweden, Norway) and Northern Europe (Denmark, Netherlands, and Luxembourg). Here P10s

average 58 percent of the median and decile ratios are about 3.0 or less. The Czech Republic

comes in about average here (though inequality has risen since this date by most accounts). We

also note that there are no G-20 nations represented here.

Central Europe comes next (Germany, Belgium, Austria, and France) with decile ratios

from 3.18 to 3.54, and ginis from .255 to .2.88. The figures for Germany include East Germany

as well as West Germany. And the first two G-20 nationsGermany and France first appear

(Table 1).

Taiwan is an anomalous entry in the middle of the table, with a gini (.277) and decile ratio

(3.38) in the middle European range. Spain, Poland, and Switzerland also form a curious group

in the middle. Canada appears next with a lower gini (.315) and decile ratio (4.13) than any other

Anglo-Saxon nation and with less inequality than is found in Hungary, Ireland, Israel, or Italy.

Japan has more or less the same income distribution characteristics, as does Canada, though the

only estimate we have and trust is now a decade old.

Italy (4.77) and the English speaking countries of Australia (4.33) the United Kingdom

(4.57), and the United States (5.54) come next with still higher levels of inequality. The highest

levels of inequality and social distance that we can measure with good confidence are in Russia

and Mexico.

While percentile ratios as measures of social distance have some obvious appeal (e.g.,

insensitivity to topcoding,5 ease of understanding), they have the disadvantage of focusing on

only a few points in the distribution and lack a normative basis. Figure 1 presents an alternative

more commonly employed Lorenz-based summary measure of inequality, the gini coefficient.

As we saw above, relying on this measure, country rankings change little. Inequality is still


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lowest in Scandinavia, then Central Europe, Southern Europe, and Asia with the English

speaking countries (except for Canada) having the highest inequality, and the United States the

highest among these, and then followed at last by Russia and Mexico. The other Central

European nations show no clear pattern, and both Taiwan and Japan are close to the middle of

the ranges displayed here. In sum, there is a wide range of inequality among rich and middle-

income nations covered by LIS.

Just The 12 G-20 Nations

We can add two more G-20 nations to the 10 in Figure 1, by including the two Latin

American G-20 countries from the IDB data harmonized by Szekely and Hilgert (1999a, 1999b)

to reach 12. We have grouped them geographically in Table 1, into five groups, with Latin

America, European OECD nations, Anglo-Saxon OECD nations, Eastern Europe, and Asia (the

latter two being represented by Russia and Japan alone). The range is now widened even further

with Brazil and Argentina (albeit the urban areas only) having ginis of .571 and .442,

respectively, though we suspect that the true level of inequality in Argentina is higher than that

shown here due to omission of the rural areas in the Szekely and Hilgert database. The same

clusters seem to hold, with Europe, then Asia (Japan), then the Anglo OECD countries, Russia

and Latin America having the most inequality.

There are no comparable, harmonized estimates for China, India, Indonesia, Korea, South

Africa, Saudi Arabia, or Turkey (the other seven countries in the 19-nation G-20!). However,

with a little work on the part of these nations and willingness to share their data with LIS and

with other similar bodiese.g., within the G-20 itselfeven more comparable measures of

overall inequality could be developed, and key nations such as China and India could be added to

this table. Moreover, added observations for earlier years data could also be used to create time

series for all of these nations.


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That is, there exists a foundation of data sources from these nations and from the World

Bank and other data providers, which could be mobilized and harmonized to better illustrate the

level and trend in inequality in the entire G-20, and to better understand the policy issues which

affect and are effected by globalization and increased trade within and across these economies.

Explaining the Differences

There have been few attempts to explain the differences we find in economic inequality

across the rich nations (Jacobs and Gornick 2001; Jencks 2002; Gottschalk and Smeeding 1997,

2000; Gustafsson and Johansson 1997), so what we have here is piecemeal, but still instructive

explanation of initial explorations of these differences.

First, it is important to note, that explanations of differences in inequality across countries

differ according to which end of the income distribution one is addressing. That is, rather than

ad-hoc decompositions of aggregate indices, often more can learned from addressing the

explanations of the differences in incomes at each end of the income distribution separately. For

instance, low incomes (10-50 ratios or poverty rates) are quite well correlated with the

prevalence of low-wage workers within each nation (Figure 2) and with levels of non-elderly

social transfers within each nation (Figure 3). The effects of different policies to raise wages,

e.g., by administrative fiat (minimum wages) or by increasing labor productivity, are clearly

raised by this relationship.

Countries that have many jobs at low wages, United States, Canada, and the United

Kingdom, tend to have lower 10/50 ratios than do nations with higher wages at the bottom end.

Of course, many nations with higher minimum wages also suffer higher rates of unemployment.

But unemployment is not highly correlated with 10/50 ratios (or gini coefficients) across OECD

nations, largely because those nations with the lowest fractions of low-wage workers have


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generous income transfer systems which provide low-income, unemployed workers with high net

disposable incomes (see also Gustafsson and Johansson 1997; Gottschalk and Smeeding 1997).

Similarly, the relationship between cash social transfers to the nonaged and low incomes

as measure by the 10/50 ratio is also strong (Figure 3).6

Countries that spend less on their safety

nets suffer higher levels of inequality as measured by the 10/50 ratio. Social insurance against

falls in consumption due to illness and other factors are not widely available in many middle-

income countries (e.g., see Gertler and Gruber 2002, on Indonesia). Social benefits also have

fallen drastically in both value and frequency in most transition economies of Central Europe.

Thus, Mexico and Russia are just two examples of what one would find were we able to extend

this chart to other middle-income nations.

Other explanations for differences in incomes and inequality across nations are many and

complex, especially as they affect incomes at the top of the distribution. First, consider the

arguments that the United States is richer than other nations because it is more efficient. Jencks

(2002) recently addressed this question using LIS data and OECD data, summarized in Table 2.

He concludes that one major reason the United States is richer is because we employ more

people who work longer hours than do their counterparts, in say Germany or France. When he

corrects Gross Domestic Product (GDP) per capita for hours worked, and labor force

participation, GDP per hour is actually about the same in the United States than in Germany or

France. Correcting for unemployment, by adding the total number of hours unemployed workers

in these countries want to workeven if unemployed (GDP per available hour)does not

change this result.

While these data say nothing about inequality, per se, the number of hours worked is

clearly an important ingredient for measured inequality (just as the distribution of wage rates are

important). But other studies of Germany and the United States (Devroye and Freeman 2001),


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and a set of countries including Canada and Germany (Jacobs and Gornick 2001), indicates that

not only do United States workers work more hours overall, but high-income United States

workers work many more hours per year than do their counterparts in other nations. Moreover,

high-income United States workers are more likely to be married to spouses who also work

multiple hours than in other nations (Jacobs and Gornick 2001). While the effects of these

differences are yet to be completely and systematically worked out, the amount of work effort at

each end of the distribution, as well as the reward for that work, are both clearly important. And

it appears that both the rich and the poor in the United States work more hours than do their

counterparts in other rich nations (Osberg 2002).

Closely tied to the number of hours worked and earnings are demographic differences in

household composition across nations. In general, nations with relatively higher levels of

immigrants and relatively more single parents will have greater inequalities, especially at the

lower end of the income distribution, than do nations which have fewer single parents and lower

levels of immigration, all else equal. But the fraction of elderly households in a nation does not

affect income distribution comparisons across countries largely because the elderly have levels

of inequality that are similar to those of the nonelderly (Osberg 2000). Casual comparisons of the

high immigrant, high single-parent, AngloSaxon countries (e.g., Canada, Australia, the United

Kingdom, and the United States) with central and northern Europe tend to bear out this finding

well.

Other factors are less easily accounted for. Many authors find that labor market

institutions, especially collective bargaining, wage setting, levels, and penetration of minimum

wages, are important for determining the level of inequality in wages and earnings across nations

(Gustafsson and Johansson 1997; Gottschalk and Smeeding 1997). Differences in educational

attainment are also important as the better educated earn more than the less well-educated, all


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else equal, in every country (see Rehme 2002a, 2002b; Smeeding and Sullivan 1998). But recent

evidence suggests that it is the former (institutions) rather than the latter (skills per se) that is

more important in explaining differences in the cross-section. Blau and Kahn (2001) find that

workers within single categories of education and adult test scores in the United States (e.g., high

school graduates with median level skills as measured by the OECD individual adult cognitive

literacy survey), have distributions of wages and earnings which differ amongst themselves by

more than does the entire distribution of wages differ (across all skill and education groupings)

in Germany, Netherlands, and Sweden. The differences in wage setting institutions across

countries therefore account for many of the differences in pay that we find at any point in time.

Finally, consider the arguments of Frank and Cooks (1996) book, The Winner Take All

Society. In an increasingly global economy, where markets are ever widening, where pay is tied

to output and productivitynot only for chief executives and business men, but for professionals

(like lawyers, physicians, and scientists) as well, and where labor and firms can migrate to the

highest profit areas, we expect that the wage distribution at the top of the market will continue to

widen, as it has in some nations, notably the United States and the United Kingdom, but now

also in Sweden, Germany, France, and Canada.

Summary

There exists a wide range of inequalities across the nations of the rich world and the rich

nations of the G-20 as well, though the range across the rich G-20 members is narrower because

the high equality nations of Scandinavia and Northern Europe are not represented. And adding

the comparable data we have on Russia and Mexico, not to mention fairly comparable data for

Argentina and Brazil, suggests that even wider ranges of inequality are found as we move down

the development ladder to the middle-income nations.


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The explanations of these differences at a point in time are many, and to quote one article

on this topic, there is no one smoking gun explanation. Public policies toward the poor and

jobless, the multiple institutions of the labor market, levels of education and training,

demographic differences and even hours worked, all can play a role in explaining these

differences at a point in time.

But, regardless of these differences, economies are not fixed but rather dynamic and ever

changing, as this conference attests. Hence explanations of the trends in inequality across nations

may be more important than explaining levels of inequality at any point in time. Certainly, the

literature on this topic suggests that trends in inequality of both earnings and income are more

readily studied and across a wider range of nation, even if the data used to make these studies is

not the best we have available (Atkinson and Brandolini 2001).

IV. Trends in Inequality

Do the differences in inequality in OECD countries in the late 1980s and 1990s reflect

convergence to a common level of inequality or are the less equal countries (the United States,

the United Kingdom, Russia, and Mexico) becoming even less equal? To answer these questions,

we compare recent trends in inequality (from 1979 onwards). Because the LIS data cover only

two to five data points in each nation, we also rely on published and unpublished data from other

sources to assess the trend in income inequality (Gottschalk and Smeeding 1997, 2000;

Gottschalk, Gustafsson, and Palmer 1997; Frtser 2000; Atkinson and Brandolini 2001;

Atkinson, Rainwater, and Smeeding 1995; Atkinson 2000) to analyze differences across rich

nations.

While differences in units, income measures, equivalence adjustments and other factors in

different studies make it difficult to compare levels of inequality across these studies, trends in


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inequality will be more comparable than are differences, as long as income concepts, surveys

(and their methodologies) and inequality measures remain constant within countries over time

(Gottschalk and Smeeding 2000). Unfortunately, nations do not always follow this rule. But

taking advantage of a series of adjustments when assessing the trend in income inequality within

any single nation and across nations, we are able to piece together a rather robust story for the

rich nations of the world (Atkinson, Brandolini, and Smeeding 2001; Smeeding and Grodner

2000).

As we begin this investigation, one should be warned that we are assessing mainly

differences within the rich nations of then G-20 and to a much lesser extent the differences

among the middle-income nations (Mexico and Russia) and the lower-income, but much larger

nations, e.g., China and India with about one-third of the worlds population. The trend in global

inequality depends not only on income distribution changes within any set of nations, but also on

the growth of average incomes across nations. Hence, rapid economic growth within China and

Indiaeven when inequalities are also increasing within these nations, can drastically reduce

world income inequality (Quah 2002; Sala-i-Martin 2002). We do not address the question of the

rates of growth within poor nations compared to rich nations, as do others (Sala-i-Martin 2002;

Dowrick and Akmal 2001; Dowrick and DeLong 2001). Ideally, one would want to use

Purchasing Power Parities (PPPs) to changes incomes for a comparable set of national household

surveys into one single survey and then to compare the levels and changes in incomes for all

respondents in every sample in all nations. However, that task is not yet accomplished, except for

the European Countries (see Belbo and Knaus 2000). And the development of key data, such as

directly measured PPPs for China, is needed to make this exercise even more meaningful.


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Trends in Income Inequality Over Timethe Evidence from LIS and Elsewhere

In general, nations with multiple data series from different sources, and counties that

clearly identify survey differences and changes in survey practices over time, provide the best

sources of distributional trend comparisons. Nations with very few data points and those with not

well-identified survey practices or concepts do not always provide accurate sources for trend

analysis. Decisions about which nations to include and exclude, based on data quality

considerations, should be at the forefront of the users agenda. Many of these issues have been

raised by others (Atkinson and Brandolini 2001; Gottschalk and Smeeding 2000; Atkinson,

Rainwater, and Smeeding 1995), so we do not delve deeper into them here. The Canberra Group

(2001, chapter 9) offers a convenient summary of pitfalls for those who desire such a technical

review.

Given these differences, we should go slowly and carefully when assessing trends in

economic inequality across and within nations. For instance, LIS does its best to guarantee

differences in inequality measurement at a point in time, and is less well suited for measuring

changes in inequality over time. For most nations, LIS has few data points. Moreover, in

choosing the best data for comparisons at a point in time, different surveys are used in different

nations. For instance, in Germany, three different datasets have been used by LIS, and these

three do not lend themselves easily to trend analyses. Even though LIS is careful to note when

different datasets, income definitions, or other changes take place in national datasets, the

availability of data alone does not guarantee its consistency over time. Over these past 20 years

of normalizing microdata to a common definition, many of the cautions urged above have been

learned from trying to assess inequality trends using LIS. Survey practices and data quality have

changed in most of the countries found in Table 1. In some cases, a new survey replaces the old

(Australia 1994). In others, panel datasets (Luxembourg and Germany), which provide the LIS


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cross-sections, have suffered from sample attrition and some have not added new immigrants to

their original samples for LIS. Many nations provide income distribution trend data, based on

national definitions of income that include income items not included in LIS income such as

capital gains (Sweden), and imputed rent (the Netherlands), while several others typically

exclude near cash income such as food stamps in the United States. Finally, the weighted sum

total of aggregate incomes taken from the surveys in several countries may be substantially

below somewhat comparable aggregate national incomes suggesting that income underreporting

may be a serious issue (e.g., Italy, Spain; see Smeeding, Rainwater, and Burtless 2001). While

the changes found in LIS may be reasonable, they should be compared to those from other

sources, which are designed to produce more accurate trend data.

The data on trends in income inequality have grown dramatically in recent years. When the

Atkinson, Rainwater, and Smeeding (1995) report was published, there was evidence that among

16-18 countries observed during the 1970s and 1980s, the trend in inequality observed from

comparable gini coefficients could be separated into two eras (Table 3, first and second

columns). From the mid 1970s to the mid 1980s, inequality increased in only the United

Kingdom and the United States, falling modestly in seven other nations and having no trend in

nine others. These increases in the United States and the United Kingdom were in marked

contrast to the falling inequality in both nations from 1950-1970 (Gottschalk and Smeeding

2000). There were no suitable and accurate data in seven other nations for the 1970s or 1980s

(see na in first and second columns Table 3).

By the time the 1980s were finished (middle column, Table 3), inequality was falling

significantly only in Italy, but was increasing in nine nations, while eight experienced no change,

where a change in measure of plus or minus 1 percent in a given measure is taken as an

insignificant change. Inequality in the United Kingdom increased by over 15 percent over this


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period, while inequality in the United States rose by about 12 percent. Inequality either stopped

declining or rose modestly in all of the other nations shown here during the 1980s.

Finally, a combination of results for 25 nations are shown in the last column of Table 3,

using LIS, and similar summaries of other national trends based on data collected by the OECD

(Frster 2000), by Atkinson (2000) and from recent national reports. Here we see that from the

late 1980s to the mid to late 1990s inequality rose in almost every OECD nation, with Denmark

being the only possible exception. Large increases were experienced by only two nations, and by

the late 1990s inequality increases had become more tempered in the United Kingdom, and also

in the United States. These trends may in time, to be shown to have been a result of the strong

labor markets and low unemployment in these nations, during the latter half of the 1990s.

But inequality has begun to increase in Canada, France, and Germany in the 1990s, where

before this time it had not risen. Russian and Czech inequality began to rise in the 1990s as one

might expect given the suppression of market earnings distributions under the institutions of the

former Soviet regime. However these changes have been accompanied by very different starting

and ending points in these two nations (see Figure 1 where Czech inequality is .259 in 1996, and

Russian inequality is .447 in 1995). New Zealands inequality continued to rise as well. Thus, the

patterns change considerably as we move from period to period.

Because pictures are often easier to fathom than are strings of ++ and --, Figure 4

provides a snapshot of inequality trends in seven nations. The basic diagram is taken from

Atkinson (2000) with later year data adjustments by the present author from the same sources,

where available. The data confirm the patterns seen in Table 3, and also suggest a slowing, but

not a reversal, of rising inequality in several nations at the end of the 1990s. However, they also

show a rise in Canadian inequality as the 1990s draw to a close.

The following summary impressions can be gleaned from Table 3 and Figure 4:


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The OECD study (Frster 2000) focused on the 1980s that were a period of transition

from one period (flat or declining inequality) to another period (rising inequality) in

most nations. As Gottschalk and Smeeding (2000) argue, this best describes a U-

shaped change in the distributions of income in most nations with inequality falling in

the 1960s (few comparable observations), and early 1970s, but then rising from the late

1970s and 1980s into the 1990s. The turning points (bottom of the U) differ across

nations. Many (e.g., the Scandinavian nations) did not experience a rise in inequality

until the 1990s. And in many nations (e.g., Germany, France, and Canada) these

increases have so far been very modest (see Gottschalk and Smeeding 2000, for more

on the U shape).

While inequality rose rapidly in the United Kingdom and the United States during the

1980s and early 1990s, the trend seems to have flattened out in both countries by the

end of the decade. To the extent that the United Kingdom income distribution source

(Family Expenditure Survey) and United States source (Current Population Survey) do

not accurately capture or measure incomes in high-income households (due to top

coding, non-response, etc.), this conclusion may be unwarranted (e.g., see

Congressional Budget Office 2001, for the United States 1979-1997; and Jencks 2002).

However, the rate of increase in inequality has still slowed markedly in these two

nations in the late 1990s.

LIS data for Mexico and Russia shows much more volatility than do the other datasets.

Inequality in Mexico was lower in the late 1980s than in 1990s but inequality was

much higher in both 1994 (gini of .496) and 1998 (.494) than in 1996 (.477), perhaps

due to cyclical volatility. And several studies (e.g., Hlscher 2001) based on LIS and

other data argue for rapidly rising inequality in Russia in the 1990s.7

Other world


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pictures are somewhat more mixed. For instance, Sala-i-Martin (2002, Appendix

figures) taken from the World Bank data compiled by Deininger and Squire (1996)

suggests that inequality rose in China and Indonesia, but not in India, Brazil, or

Pakistan over the 1970-1997 period. The refinement of these analyses must await better

data and methods (e.g., Deininger and Squire 2002).

What Changed and Why?

The estimates in Table 3 and Figure 4 provide an overall picture of changing inequality, but

one that needs to be carefully interpreted. For instance, suppose that one weights changes in

inequality at the bottom of the distribution more than changes at the top? If so, one would be

happy to learn that overall changes in relative poverty, e.g., the percent with incomes less than 40

or 50 percent of the adjusted (for family size) median were far less frequent and were of lesser

magnitude than were increases in overall inequality in rich OECD nations (Smeeding, Rainwater,

and Burtless 2001). That is, in most of the European countries studied here and in the United

Kingdom and the United States, relative poverty did not increase by much if at all, during the

1990s. Thus, the phenomenon of increasing inequality is predominately a consequence of

changes in the top of the distribution, rather than in the bottom (Frster 2000).

The data say nothing about tradeoffs between economic growth and inequality in rich

nations. Though much has been written on this topic in recent years, there is no compelling case

for one being systematically related to the other in OECD nations (e.g., see Arjona, Pearson, and

Ladaique 2001, for a concise summary of studies in OECD nations). In fact, in some rapidly

growing nations, such as Ireland, a modest increase in inequality can be seen as a small price to

pay for rapid economic growth in real incomes and falling poverty at all levels of the income

distribution (Nolan 2001). Similarly, modest increases in inequality may be the price that needs


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to be paid by countries such as Canada, France, Germany, and Australia, as they adjust to greater

trade and the increased capita and labor mobility that accompanies globalizing economies.

Finally, the question is raised whether increases in inequality were accompanied by

widespread or selective changes in real economic well being within each nation. The question of

whether all the boats rose or only some, while others sank, is clearly a critical one for most

nations. As in Ireland, rising inequalities are much more acceptable when living standards are

rising across all segments of the population than when they are concentrated among the rich

alone. While we are trying to compile these data for a number of countries, the experience of the

United States is one which other countries might chose not to emulate in this regard.8

Figure 5

suggests that America experienced several distinctly different periods of income inequality

change during the past 50 years: first, one of falling inequality and widespread real income gains

largely in concert for all families from roughly 1950s through the mid 1970s; second, one where

real income growth was increasingly different depending on where one lies in the income

distribution from the 1970s onward. And within this latter period we note two different epochs.

While average family incomes grew during the 1980s, and especially the period from 1993

onward (albeit reflecting the cyclical changes of the 1991-1993 recession), higher incomes grew

by much more than did lower incomes throughout the period. Lower incomes fell from 1979

until 1993 before rising markedly in the later 1990s. Still, by the end of the 1990s, the average

income for families in the bottom fifth of the distribution had barely reached the real standard of

living experienced at the end of the 1970s, despite the real income gains for all during the latter

1990s.

Explanations for why income inequality changed in rich nations are many and, as seen in

the data for the United States, can be very complicated as well. Many of these comparisons are

based on LIS data (Rehme 2002a, 2002b; Acemoglu 2002; Gustafsson and Johansson 1997).


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Others are based on series of national datasets (Frster 2000; Arjona, Pearson, and Ladaique

2001). Still others concentrate on earnings changes alone and are not based on changes in overall

incomes, after taxes and transfers (Card and DiNardo 2002; Beaudry and Green 2000).

First, it is important to establish what these studies do not show, i.e., that increasing levels

of international trade can be tied to growth in inequality. To quote Friedman (2000), patterns of

change in wages and earnings are not determined in Beijing, but are a product of a complex set

of interactions within and across nations. More likely, the effect of international trade on the

economy is proportionate to the size of the trade sector in each nation (Richardson 1995). Studies

that have tried to establish this connection using LIS data have concluded that greater levels of

trade do not lead to increased poverty or inequality (e.g., Gustafsson and Johnsson 1997; Osberg

and Sharpe 2000; Osberg 2000).

There is, however, evidence that both the changing supply and demand for labor of

different skills can explain some of the changes in earned incomes across rich nations, and

possibly among middle-income ones as well. The rising demand for skill led to higher (lower)

wages in countries that had smaller (larger) responses in their education (supply) sectors. Thus,

Canada and the Netherlands experienced much smaller increases in high wages than did the

United States or the United Kingdom (Gottschalk and Joyce 1997). Institutional mechanisms

have also slowed the rewards to higher skills in many European nations, at least early into the

1990s (Katz and Autor 2000). And there is new evidence that the demand for skills increased

faster than the supply in middle-income nations as well, (Berman and Machin 2001) and in

Mexico (Legovoni, Bouillon, and Lustig 2002), thus exacerbating earned income inequality.

It is more difficult to tie these explanations to skill biased technological change or to

demand side effects as various sectors of the economy have experienced different levels of

technological change in each country as well as across countries. Different practices of


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management, different national climates, and institutions for promoting for entrepreneurship, the

differential availability of venture capital, and diffusion of technological progress are also

apparent throughout the OECD world (e.g., Frster 2000; OECD 2001). Better identification of

demand side effects is certainly needed. For instance, an interesting new paper by Acemoglu

(2002) argues that wage compression in Europe might have led to a more rapid adoption of

technology that benefited low-skill workers than in other countries.

Moreover, no one has yet documented the effects of increased changes in product quality or

the effect of falling international prices for traded goods due to greater international competition

amongst the rich nations. Our textbooks tell us that trade and comparative advantage bring a

better standard of living (more real income) to each nation, but the research that we have so far

reviewed has not addressed the size of these gains as of this writing.

Summary of Trend Analyses

It appears that the quality and quantity of consistent and good quality information on

income distribution trends is on the rise. Recent work by Atkinson (2000), Atkinson and

Brandolini (2001), the Canberra Group (2001), and the Frster (2000), in conjunction with LIS,

has made some headway into the issue, but much needs to be done to produce more consistent

and comparable measures of income inequality in most of the middle income countries and in

some of the rich ones. To the extent that these data emerge, we will be in a better position to

model the determinants of changes in inequality and to understand its evolution on a worldwide

scale.

As Atkinson (2000) concludes, rising economic inequality is not inevitableDenmark

seem to present at least one exception to the rule. However, rising income inequality is

predominant in most nations, even the most egalitarian advanced welfare state nations of the

world. And while inequality has increased, our reading of the LIS data, and to a lesser extent the


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international trend data, suggest that there have been different patterns in the timing and extent of

the increase in inequality in most nations. Moreover, national changes in inequality may have

different welfare implications depending on whose incomes are changing. In Sweden, Germany,

Norway, and Finland, most of the higher inequality in the 1990s seems to be coming from

movements at the top of the distribution (from changes in P90s), not from changes in the bottom

(i.e., from the P10s; see Gottschalk and Smeeding 2000). And most rich countries have been able

to protect the least skilled from the negative effects of rapidly changing industrial and

employment effects brought about by increased trade and technological change. At least in

theory, the winners from the globalization game should be able to compensate the losers to the

benefit of all. And the strong welfare states of Europe and Scandinavia seem to have been able to

protect their least skilled and least well off citizens better than have many others during this

period.

That said, only a few authors have begun to sort out the sources of differences in inequality

trends across the rich countries, and even fewer in the middle income and poorer nations. Much

additional work is needed here.

V. Summary and Conclusions

This brief paper has perhaps asked more questions than it has given answers. This is how

the paper was meant to be written. Understandings and explanations of changes in the broad

structures of economic inequality within and across nations depend heavily on the quality of the

data that we have at our disposal. For social scientists interested in this topic, economic

inequality data is equivalent to the astronomers Hubbell telescope or the geneticists Human

Genome project. Without accurate indicators, model building and hypothesis testing cannot


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adequately proceed. Cross-national data on income distribution will never be perfect. But the

ratio of signal to noise in these data can still be improved, as the LIS project has demonstrated.

And there is room for the non-LIS G-20 nations to create similar datasets to illustrate changing

economic inequality in their nations as well.

The evidence that we do have suggests that globalization is one force among many which

for widening income inequalities in the rich countries of the OECD. The relationship between

economic inequality and growth has not been sorted out, even in the rich nations, and we have

yet to determine the effect of very high levels of inequality on civic engagement, or on support

for policies which enhance opportunity for all citizens. Still globalization in rich nations appears

to act more by raising incomes at the top of the income distribution than by lowering them at the

bottom. Notwithstanding, this influence, however, domestic policieslabor market institutions,

welfare policies, etc.can act as a powerful countervailing force to market driven inequality.

Even a globalized world, the overall distribution of income in a country remains very much a

consequence of the domestic political, institutional and economic choices made by those

individual countriesboth rich and middle income ones.

.


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Endnotes

1. The Canberra Group of National Statistical Offices and Organizations (including LIS,

the World Bank, the United Nations and others) produced its final report on international,

standards for income distributions last year. See Canberra Report (2001) orwww.lisproject.org for a summary of all of the Canberra meetings and the final report

2. The United Kingdom data is the only exception to this rule as their Family ExpenditureSurvey (FES) uses a bi-weekly accounting period with rules for aggregating up to annual

totals. In Germany, LIS has aggregated the monthly and quarterly data into annual

income amounts.

3. However, for Sweden and Canada more restrictive nuclear family (Sweden) and

economic family (Canada) definitions of the accounting unit are necessary (see Atkinson,Rainwater, Smeeding 1995, Chapter 2, for additional details).

4. For more on absolute or real income differences, see Rainwater and Smeeding (1999)

and Gottschalk and Smeeding (2000).

5. Topcoding is the procedure by which nation place a maximum value on reported incomes

in the public release version of a survey. In countries with rapidly growing high incomes,arbitrary topcodes can have serious effects on measured inequality (e.g., Smeeding and

Grodner 2000).

6. Here we have excluded transfers to the elderly, but even when they are included, the

same relationship holds (see Smeeding 1998; Smeeding, Rainwater, and Burtless 2001).

7. However because the Mexican and Russian surveys are taken a over a period of several

months when inflation can be rapid, the estimates of annual inequality for each nationmay be sensitive to the treatment of changes in domestic prices over this period.

8. Figure 5 is based on the U.S. Census Bureaus income series for families of two or morepersons (thus omitting unrelated individuals), unadjusted for taxes paid, but gross of

transfers received. It is therefore a less complete income concept and population group

than the one studied by LIS. However restricting ourselves to this definition buys a more

or less consistent 50-year series of incomes and income inequality. We are currentlytrying to develop a series that is both consistent with LIS and with national survey

practices, measures of price change, etc., for several countries.

Globalization, Inequality, And the Rich Countries of the G20 - Evidence From the Luxembourg Income Study

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